Description Usage Arguments Details Value Author(s) References See Also Examples
Imputes missing data in a categorical variable using multinomial Log-linear Models.
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formula |
a formula expression as for regression models, of the form
|
data |
A data frame containing the incomplete data and the matrix of the complete predictors. |
drop.unused.levels |
Drops unused levels. |
start |
Starting value for bayespolr. |
maxit |
Maximum number of iteration for bayespolr. The default is 100. |
missing.index |
The index of missing units of the outcome variable. |
... |
Currently not used. |
object |
|
x |
|
y |
Observed values. |
main |
main title of the plot. |
gray.scale |
When set to TRUE, makes the plot into gray scale with predefined color and line type. |
multinom
calls the library nnet. See multinom for other details.
model |
A summary of the multinomial fitted model |
expected |
The expected values estimated by the model |
random |
Vector of length n.mis of random predicted values predicted by using the multinomial distribution |
residual |
The residual vector of length same as y |
Masanao Yajima yajima@stat.columbia.edu, Yu-Sung Su ys463@stat.columbia.edu, M.Grazia Pittau grazia@stat.columbia.edu, Andrew Gelman gelman@stat.columbia.edu
Yu-Sung Su, Andrew Gelman, Jennifer Hill, Masanao Yajima. (2011). “Multiple Imputation with Diagnostics (mi) in R: Opening Windows into the Black Box”. Journal of Statistical Software 45(2).
Andrew Gelman and Jennifer Hill, Data Analysis Using Regression and Multilevel/Hierarchical Models, Cambridge University Press, 2007.
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